LRQVB: Low Rank Correction Quantile Variational Bayesian Algorithm for
Multi-Source Heterogeneous Models
A Low Rank Correction Variational Bayesian algorithm for high-dimensional multi-source
heterogeneous quantile linear models. More details have been written up in a paper
submitted to the journal Statistics in Medicine, and the details of variational
Bayesian methods can be found in Ray and Szabo (2021) <doi:10.1080/01621459.2020.1847121>.
It simultaneously performs parameter estimation and variable selection. The
algorithm supports two model settings: (1) local models, where variable selection
is only applied to homogeneous coefficients, and (2) global models, where variable
selection is also performed on heterogeneous coefficients. Two forms of parameter
estimation are output: one is the standard variational Bayesian estimation,
and the other is the variational Bayesian estimation corrected with low-rank adjustment.
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